The present disclosure relates generally to inspection systems and, more specifically, to inspection systems and methods for capturing data from process indicators and validating the integrity of the collected data.
Machine monitoring and diagnostics can be seen as a decision-support tool which is capable of identifying the cause of failure in a machine component or system, such as a power generator, as well as predicting its occurrence from a symptom. Without accurate detection and identification of the machine fault, maintenance and production scheduling cannot be effectively planned and the necessary repair tasks cannot be carried out in time. Therefore, machine monitoring and diagnostics are essential for an effective, predictive maintenance program.
At least one purpose of using machine monitoring and diagnostics is to increase equipment availability, as well as reduce maintenance and unexpected machine breakdown costs. In order to maximize availability, system managers often work to increase reliability by maximizing the machine uptime and, at the same time, increase maintainability by minimizing the mean time to repair. As a result of monitoring and diagnostics, the frequency of unexpected machine breakdown may be significantly reduced, and machine problems may be pinpointed more quickly.
In some known monitoring systems, machine monitoring and diagnostics may be done by simply listening to the sound generated during machine operation, or by visually examining the quality of machined parts to determine machine condition. However, many machine faults, for example, wear and cracks in bearings and gearboxes, are not accurately assessed by relying only on visual or aural observations, especially during operation. In some known systems, operators collect data on machine conditions through visual inspection of machine process indicators, and may enter such data into a historical data tracking computing system. However, such systems are prone to operator error. For example, an operator may misinterpret or misread a generator process indicator, for example, the operator may perceive a process indicator value of 5.605 from a digital process indicator that is actually showing a value of 5,605, or may read the wrong generator process indicator for example, collect and input a process indicator value from the wrong generator, or from the wrong process indicator at the correct generator.
Therefore, more sophisticated data collection and analysis techniques have been developed to help the maintenance technician and engineer collect data used for detecting and diagnosing machine failures.